Rapid mapping of alloy surface phase diagrams via Bayesian evolutionary multitasking
作者机构:Department of Energy Conversion and StorageTechnical University of DenmarkAnker EngelundsvejBuilding 3012800 Kgs.LyngbyDenmark
出 版 物:《npj Computational Materials》 (计算材料学(英文))
年 卷 期:2023年第9卷第1期
页 面:905-918页
核心收录:
学科分类:08[工学] 080502[工学-材料学] 0805[工学-材料科学与工程(可授工学、理学学位)]
基 金:The authors thank the financial support from the BIKE project:BImetallic catalysts Knowledge-based development for Energy applications The BIKE project has received funding from the European Union’s Horizon 2020 Research and Innovation program under the Marie Skłodowska-Curie Action-International Training Network(MSCA-ITN),grant agreement 813748 The authors also thank the Villum Fonden for funding through the project V-sustain(No.9455) the Niflheim Linux super-computer cluster installed at the Department of Physics at the Technical University of Denmark for providing computational resources
摘 要:Surface phase diagrams(SPDs)are essential for understanding the dependence of surface chemistry on reaction *** multi-component systems such as metal alloys,the derivation of such diagrams often relies on separate first-principles global optimization tasks under different reaction *** we show that this can be significantly accelerated by leveraging the fact that all tasks essentially share a unified configurational search space,and only a single expensive electronic structure calculation is required to evaluate the stabilities of a surface structure under all considered reaction *** a general solution,we propose a Bayesian evolutionary multitasking(BEM)framework combining Bayesian statistics with evolutionary multitasking,which allows efficient mapping of SPDs even for very complex surface *** proofs of concept,we showcase the performance of our methods in deriving the alloy SPDs for two heterogeneous catalytic systems:the electrochemical oxygen reduction reaction(ORR)and the gas phase steam methane reforming(SMR)reaction.